4.9
16,611 条评分
★★★★★
2026年4月20日
2021年10月15日
更新内容
v1.21.0Bug fixes and improvements
应用信息
- 开发者
- Opus Training, Inc.
- 分类
- Business
- 价格
- 免费
- 版本
- 1.21.0
- App ID
- 1589513546
简介
Opus helps businesses train their entire frontline workers over mobile. We work with hundreds of growing multi-unit hospitality and restaurant brands, franchisors, independent businesses, and more. Note: employees need access from their company to download Opus. Opus is the employee training platform purpose-built for frontline teams. Help 100% of your staff learn and build skills quickly with hands on, accessible training. Employees learn through short, multi-media-rich, interactive training or access resources. Written and video content is auto-translated into over 100 languages. Frontline managers save time with tools to assign trainings, send real-time message communications, and track progress. Training & operation teams can easily build trainings (no instructional design skills necessary!), upload resources, send messages, set assignment workflows, and get real-time reporting. Great for: - Onboarding and new hire training - SOPs and procedure changes - Reinforcement or just-in-time training - Manager training and career pathing - State-accredited Sexual Harassment Prevention compliance training - Accredited food safety certifications Key features: - For trainees: native mobile app, no email required for login, resources, personalized learning paths, auto-translation into 100+ languages, ability to provide feedback/ratings. - For managers: native mobile app, simple training task list for digital and on-the-job live training, tools to help create or provide training feedback, and one-way message communications to store teams. - For admins & businesses: native content builder optimized for mobile, media-rich micro-learning. Over 500+ off-the-shelf editable course templates. Robust organization and user permissions suitable for franchisors, multi-concept groups, and regional/national chains. Integrations to 35+ HRIS systems and real-time reporting.
下载量预测
预估总下载量
30K
低 / 月
40K
预估 / 月
59K
高 / 月
基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。
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